Face recognition systems have been quite popular in today's commercial and entertainment businesses. Face recognition in videos is a technical problem in computer vision that targets at locating and identifying faces in a video sequence by a given set of images that contain the faces with known identities. For example, video face recognition has been driven by its huge potential in developing applications in many domains including video surveillance security, augmented reality, automatic video tagging, medical analysis, quality control, and video-lecture assessment. Even though the face recognition is a relatively easy task for human brains, it is challenging for machines due to large variations in appearance of identified objects in terms of orientation, illumination, expression and occlusion.
Many challenges exist for the face recognition using currently-available techniques. Recently, face recognition (FR) via sparse representation-based classification (SRC) and its extensions have proven to provide state-of-the-art performance. The main idea is that a subject's face sample can be represented as a sparse linear combination of available images of the same subject captured under different conditions (e.g., poses, lighting conditions, occlusions etc.). The same principle can also be applied when a face image is represented in a lower dimensional space describing important and easily identifiable features. In order to enforce sparsity, l1 optimization algorithms can be employed. Then, the face class that yields a minimum reconstruction error is selected in order to classify or identify the subject, whose test image or sample is available. Sparse coding has also been proposed to jointly address the problems of blurred face recognition and blind image recovery.
However, l1 optimization methods for improved face recognition rates can only be successful under certain conditions. Specifically, the sparse representation based face recognition assumes that training images have been carefully controlled and that the number of samples per class is sufficiently large.
From a different point of view, in order to remove outlier pixels from corrupted training data, the low-rank structure of face images has been recently investigated. The low-rank structure of similar faces is explored under the assumption that the images are of some convex Lambertian object under varying illumination. To recover subspace structures from data containing errors, methods such as Robust Principal Component Analysis (RPCA) and Low-Rank Representation (LRR) have been proposed. However, the above methods are transductive and cannot remove corruptions from new data efficiently. A desired property in face recognition is not only to recover clean images from corrupted training data, but also to recover a clean image from complex occlusions and corruptions for any given test sample.
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems.